Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm

R Jayasree; N A Sheela Selvakumari

doi:10.17485/IJST/v16i38.226

Article

Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i38.226

Year: 2023, Volume: 16, Issue: 38, Pages: 3230-3235

Original Article

Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm

R Jayasree^1,2*, N A Sheela Selvakumari³

¹Research Scholar, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India
²Assistant Professor, Department of BCA, PSGR Krishnammal College for Women, Coimbatore, Tamil Nadu, India
³Associate Professor, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:18 July 2023, Accepted Date:14 August 2023, Published Date:13 October 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This work is to propose a more effective Fuzzy C-means clustering algorithm for predicting student performance based on their health. Methods: The standard dataset is collected from UCI repository. This study proposes FPCM-SPP clustering algorithm which is compared with traditional algorithms like K-Means, K-Medoids, and Fuzzy C-Means using student data from secondary education at two Portuguese institutions (2008). Based on the clustering accuracy, mean squared error, and cluster formation time, the performance of the clustering methods is compared. Findings: The proposed Fuzzy Possibilistic C-Means for Student Performance Prediction (FPCM-SPP) Algorithm, according to the observational findings, performed the best of all the models. Predicting student’s current health status at an early stage of the school can help academia not only to concentrate more on the healthy students but also to apply more efforts in developing remedial programs for the weaker ones in order to improve their progress while attempting to avoid student dropouts. Novelty: The innovative aspect of this research is that it suggests ways to improve on the performance of earlier algorithms through modifications to the FPCM's objective function.

Keywords: Prediction, Clustering, K-Means, K-Medoids, Fuzzy C-Means, FPCM-SPP

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Copyright

© 2023 Jayasree & Selvakumari. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)